Models representing data relationships and patterns, such as functions, algorithms, systems, and the like, may accept input (sometimes referred to as an input vector), and produce output (sometimes referred to as an output vector) that corresponds to the input in some way. For example, a machine learning model may be implemented as an artificial neural network (“NN”). Artificial neural networks are artificial in the sense that they are computational entities, analogous to biological neural networks in animals, but implemented by computing devices. Output in NN-based models is obtained by doing a “forward pass.” The forward pass involves multiplying large NN weight matrices, representing the parameters of the model, by vectors corresponding to input feature vectors or hidden intermediate representations. In certain systems, such as systems designed to provide customized content (e.g., content with recommendations for goods and/or services) to users based on the content interaction histories of the users, NN-based models may generate probability scores via the forward pass using content interaction histories. The probability scores may indicate the probabilities of a user performing some future interaction, such as purchasing or otherwise acquiring items during a time period. The parameters of a NN can be set in a process referred to as training.